How to do it?:
Open the Rmarkdown file of this assignment (link) in Rstudio.
Right under each question, insert a code chunk
(you can use the hotkey Ctrl + Alt + I to add a code chunk)
and code the solution for the question.
Knit the rmarkdown file (hotkey:
Ctrl + Alt + K) to export an html.
Publish the html file to your Githiub Page.
Submission: Submit the link on Github of the assignment to Canvas
gganimate and gifski
then restart Rstudio. Using the Adult Census Income data,
make an animation using geom_point and
transition_states.#install.packages('gganimate')
#install.packages('gifski')
library(gganimate)
library(ggplot2)
library(tidyverse)
library(knitr)
library(tidyverse)
setwd("C:/Users/student/Downloads")
df = read_csv('adult_census.csv')
head(df)
## # A tibble: 6 × 15
## age workclass fnlwgt education education.num marital.status occupation
## <dbl> <chr> <dbl> <chr> <dbl> <chr> <chr>
## 1 90 ? 77053 HS-grad 9 Widowed ?
## 2 82 Private 132870 HS-grad 9 Widowed Exec-manager…
## 3 66 ? 186061 Some-college 10 Widowed ?
## 4 54 Private 140359 7th-8th 4 Divorced Machine-op-i…
## 5 41 Private 264663 Some-college 10 Separated Prof-special…
## 6 34 Private 216864 HS-grad 9 Divorced Other-service
## # ℹ 8 more variables: relationship <chr>, race <chr>, sex <chr>,
## # capital.gain <dbl>, capital.loss <dbl>, hours.per.week <dbl>,
## # native.country <chr>, income <chr>
df %>% ggplot(aes(x = age,
y = hours.per.week, color=income))+
geom_point()+
transition_states(sex)+
labs(title = 'Sex: {closest_state}')
Adult Census Income data, make an animation
using geom_bar and transition_states.df %>% ggplot(aes(x = sex,
fill=race))+
geom_bar(position = 'fill')+
transition_states(education) +
labs(title = 'Education: {closest_state}')
df <- read.csv('https://covid19.who.int/WHO-COVID-19-global-data.csv')
df$month = month(df$Date_reported)
df$year = year(df$Date_reported)
df <- df %>% filter(year==2021)
library(lubridate)
df$week <- week(df$Date_reported)
d1 <- df %>% group_by(month, Country) %>% summarise(sum = sum(New_deaths))
d2 <- d1 %>% group_by(month) %>% mutate(rank=rank(-sum))
d3 <- d2 %>% filter(rank <= 10)
a1 <- d3 %>% ggplot(aes(x=rank, y=sum, group=Country, fill=Country, label=Country)) + geom_col()+
geom_text(aes(y = sum, label = Country), hjust = 1.4)+
coord_flip(clip = "off", expand = FALSE) +scale_x_reverse()+
labs(title = 'Month {closest_state}', x='', y='Total Number of New Deaths', fill='state')+
theme(plot.title = element_text(hjust = 1, size = 22),
axis.ticks.y = element_blank(),
axis.text.y = element_blank()) +
transition_states(month)+
ease_aes("cubic-in-out")
animate(a1, nframes = 400)
df <- read.csv('https://covidtracking.com/data/download/all-states-history.csv')
library(lubridate)
df$year <- year(df$date)
d1 <- df %>% group_by(year, state) %>% summarise(max = max(negative))
d2 <- d1 %>% group_by(year) %>% mutate(rank=rank(-max))
d3 <- d2 %>% filter(rank <= 10)
a1 <- d3 %>% ggplot(aes(x=rank, y=max, group=state, fill=state, label=state)) + geom_col()+
geom_text(aes(y = max, label = state), hjust = 1.4)+
coord_flip(clip = "off", expand = FALSE) +scale_x_reverse()+
labs(title = 'Year {closest_state}', x='', y='Total Number of Negative Caeses', fill='state')+
theme(plot.title = element_text(hjust = 1, size = 22),
axis.ticks.y = element_blank(),
axis.text.y = element_blank()) +
transition_states(year)+
ease_aes("cubic-in-out")
animate(a1, nframes = 400)